Accurately distinguishing true hardware failures from false alarms is a critical requirement in large-scale optical networks, where unnecessary Return Material Authorizations (RMAs) result in significant operational and financial overhead. This paper presents a novel AI-driven predictive framework that integrates multi-domain telemetry fusion, Transformer-based temporal modeling, and a domain-aware hybrid ensemble to deliver carrier-grade hardware failure detection in optical embedded systems. Unlike prior works that rely on single-sensor or threshold-based diagnostics, the proposed approach jointly analyzes optical power fluctuations, laser bias-current drift, TEC thermal instability, voltage dynamics, and DSP-layer soft metrics, enabling the model to capture degradation signatures that emerge only through cross-sensor interactions. A customized ensemble combining Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and TimeSeriesBERT is introduced to fuse complementary pattern-recognition capabilities--including long-term drift modeling, high-frequency anomaly detection, and global multi-sensor attention--resulting in superior robustness and generalization. Evaluation of real-time telemetry from optical devices demonstrates the effectiveness of the proposed system, achieving high accuracy with a high F1-score and significantly reducing unnecessary RMAs. These results highlight the novelty and practical value of the presented framework, establishing it as the first comprehensive AI solution tailored for reliable hardware-failure prediction in optical embedded systems.

As optical network functions increasingly adopt microservice architectures, traditional con- tainer security mechanisms are proving insufficient against sophisticated attacks targeting these critical infrastructure components. This paper introduces a novel framework for securing microser- vice communications in optical networks through the application of Memory Protection Keys (MPK) for enhanced container isolation. Converging containerization technologies with optical networking introduces unique security challenges, particularly in maintaining isolation between sensitive optical control functions while preserving the ultra-low latency requirements essential for network operations. The proposed approach leverages hardware-enforced memory isolation provided by Intel MPK to establish secure domains for optical control functions without sacrificing performance in latency-sensitive applications. The framework includes zero-copy communication protocols between isolated domains, runtime verification through eBPF-based monitoring, and hardware-assisted security mechanisms specifically designed for optical control plane operations. This work addresses the growing tension between security requirements and performance con- straints in software-defined optical networks, offering a balanced approach that improves container security while respecting the strict operational parameters of modern optical control systems.

Software upgrades in Optical Transport Networks (OTNs) present significant operational challenges due to the mission-critical nature of these infrastructures and the necessity for continuous service availability. Current upgrade practices often result in service disruptions, restrictive maintenance windows, and increased operational risks. This paper addresses these challenges by examining practical enhancements to existing upgrade methodologies for optical networks. The research introduces three incremental improvements to standard practices: modified A/B testing procedures adapted for OTN environments, enhanced rollback verification steps, and simplified zero-downtime techniques based on established redundancy principles. Experimental validation was conducted in a laboratory optical network environment with multiple vendor equipment. Results indicate measurable improvements in service continuity and operational efficiency compared to conventional approaches. The findings provide network operators and telecommunications service providers with straightforward, implementable guidelines for enhancing their upgrade processes in optical transport infrastructures. These improvements can be adopted within existing operational frameworks without requiring fundamental architectural changes.

The evolution of wireless mobile networks (WMNs) towards supporting advanced applications-such as next-generation smart cities, autonomous vehicles, and industrial IoT-demands enhanced data rates, lower latency, and increased network capacity. However, these advancements introduce complex security challenges, including data tampering, unauthorized access, service interruptions, and cyberattacks that threaten the integrity of WMN applications. Traditional security mechanisms, such as encryption, artificial intelligence, and access control, while beneficial, often fall short of providing comprehensive protection against these evolving threats. Blockchain technology, with its decentralized, transparent, and secure infrastructure, emerges as a transformative solution for addressing these vulnerabilities in modern wireless networks. By implementing blockchain, WMNs can achieve a tamper-proof, immutable ledger that supports secure data sharing, access management, and resource allocation. Blockchain's decentralized consensus mechanisms allow for reliable user authentication, while its transparency enhances trust among devices, making it ideal for managing data confidentiality, trust, and resource efficiency in WMNs. This paper explores the role of blockchain in fortifying security in wireless mobile networks, particularly focusing on the potential of blockchain to meet the requirements of emerging wireless standards and technologies, such as 5G and 6G, which prioritize high data throughput, low latency, and robust privacy.

With introduction of 5G, fiber to home, IoTs and growing data centers, world needs higher bandwidth to keep the world moving and connected. Along with high speed, reliability of data transfer is most important factor because of VoIP applications, Banking transactions, Stock Exchange live updates etc. All this communication needs low latency, fast and reliable data transfer. High speed connectivity is achieved by strong backhaul optical network. Fiber capacity is increased by higher rate modulations. In past decade it has been witnessed that bandwidth per wave has increased from 10G per wave to 100G, 200G, 400G, 800G, 1200G, 1600G per wave. Higher baud rates are prone to errors and reach is shorter. We propose a reinforcement-learning (RL) based framework to enhance the performance of a point-to-point optical link by dynamically adapting link parameters in response to varying channel conditions and system impairments. This method will increase the quality of signals and go for longer reach. We model the optical link as an environment in which an RL agent chooses actions (e.g., modulation format, forward error-correction rate, launch power) based on observations of current link state (e.g., Q-Factor, Signal-to-Noise Ratio, Dispersion/ Polarized Mode Dispersion (PMD) metrics, BER estimate). Through simulation and real-world emulation, we show that the RL agent converges to a policy that improves throughput and reliability compared to fixed or heuristically tuned parameter settings. This work demonstrates the potential of model-free learning methods in optical communication links, providing a path toward self-optimizing optical systems.